Time Series Analysis by E. J. Hannan is a mathematically rigorous treatment of time series theory. The book focuses on the statistical foundations of stationary processes, spectral analysis, and asymptotic theory. It emphasizes theoretical development over applied forecasting, providing deep insight into stochastic processes and estimation methods used in time-dependent data analysis.
Bayes Theory by J. A. Hartigan provides a rigorous mathematical foundation for Bayesian statistical inference. The book develops probability theory from a Bayesian perspective and explores conditional probability, decision theory, and asymptotic behavior of posterior distributions. It also addresses advanced topics such as improper priors, convergence, and nonparametric Bayesian methods. This w…
Identification of Outliers by David M. Hawkins (1980) presents a comprehensive statistical framework for detecting observations that deviate from the expected structure of a dataset. The book develops both theoretical foundations and practical methods for identifying outliers in univariate and multivariate settings. It emphasizes the impact of outliers on statistical inference, including parame…
Probability Measures on Locally Compact Groups is a mathematical monograph that develops the theory of probability measures defined on locally compact topological groups. The book studies convolution structures, random walks, and limit theorems in the context of group theory and harmonic analysis. It provides a rigorous framework connecting probability theory with abstract algebraic structures …
Conjugate Direction Methods in Optimization presents a rigorous mathematical treatment of iterative methods used to solve optimization problems. The book focuses on conjugate direction and conjugate gradient techniques for minimizing functions in finite-dimensional spaces. It develops the theoretical foundations of these methods and discusses their convergence properties and computational effic…
Theory of Statistical Experiments is a mathematical monograph that develops a formal framework for statistical experiments within modern probability and decision theory. The book focuses on the structure of statistical models, sufficiency, likelihood methods, and optimal inference procedures. It presents a rigorous theoretical treatment aimed at understanding how information is extracted from d…
Brownian Motion (Stochastic Modelling and Applied Probability) is a mathematical monograph that develops a rigorous treatment of Brownian motion as a fundamental stochastic process. The book presents both theoretical foundations and advanced analytical techniques used in stochastic modeling and applied probability. It covers construction of Brownian motion, measure-theoretic formulation, sample…
On the History of Statistics and Probability (1976), edited by Donald B. Owen, is a collection of scholarly papers presented at a symposium held at Southern Methodist University. The volume explores the historical development of statistical science and probability theory from early foundations to modern applications. It covers key contributions from mathematicians and statisticians such as Fish…
An introductory textbook presenting statistical methods with applications in business and economics. It covers descriptive statistics, probability, sampling techniques, estimation, and hypothesis testing, with practical examples for decision-making in economic and business contexts.
This book offers a rigorous introduction to probability theory, covering fundamental concepts such as random variables, probability distributions, expectation, limit theorems, and stochastic processes. It is suitable for undergraduate and graduate students in mathematics and statistics.